The goal of this observational study is to develop and validate a biomarker for lumbar myofascial pain (MP) based on ultrasound obtained measurements of the lumbar muscles and fascia. The investigators will use advanced machine learning approaches and validation in a randomized controlled trial. The main questions it aims to answer are:

• Will the deep learning-based marker reliably identify subjects from the 4 different groups: healthy, MP without trigger points, MP with latent trigger points, and MP with active trigger points?
• Will the deep learning-based marker accurately classify/predict the severity of MP in subjects with cLBP?

Participants in the healthy group will be asked to do the following tasks:

• Consent/Enrollment
• Measure Height/Weight
• Complete Questionnaires on REDCap
• Participate in Ultrasound Imaging Experiment Sessions

Participants in the chronic low back pain group will be asked to do the following tasks:

• Consent/Enrollment
• Complete Questionnaires on REDCap
• Measure Height/Weight
• Undergo a Standardized Clinical Exam
• Participate in Ultrasound Imaging Experiment Sessions

The investigators propose to use multimodal ultrasound imaging to develop and validate a practical and inexpensive biomarker for lumbar myofascial pain, which shows sensitivity to change in relation to treatment. Myofascial pain (MP) is a frequent contributing factor to chronic low back pain (cLBP). It is associated with a range of tissue abnormalities, such as taught muscle bands, trigger points (TPs), and thoracolumbar fascia motion dysfunction, along with poor tissue elasticity. As a result, a composite biomarker for MP related to components of the syndrome is more likely to be plausible biologically, robust, and useful clinically for diagnosis and treatment. The investigators propose to study: 1. The echogenicity of latent and active trigger points, 2. The dynamic spatial-temporal tissue deformation quantified by strain tensors (compression, extension, and shear) in the thoracolumbar fascia and multifidus muscle, 3. The viscoelastic properties of the fascia and muscles measured by ultrasound shear wave elastography. In the R61 Phase (year 1 to 3) the investigators will use deep learning to integrate these measurements into a predictive biomarker and use established validation methods to test its ability to predict MP.

The investigators will determine the sensitivity and specificity of the biomarker to classify the myofascial components of pain, as well as the response to treatment (a diagnostic and predictive marker).

Inclusion/Exclusion Criteria for Healthy Normals (n=40):

• No history of chronic low back pain, myofascial pain of the neck or shoulders, or fibromyalgia.
• Ages 20-70 to be age appropriate to the cLBP subjects. The investigators will recruit 10 subjects per age decile (10 subjects ages 30-39 yrs., etc.).
• Any clinically unstable systemic illness that is judged to interfere
• with the trial
• Non-ambulatory status
• Not able to complete the questionnaires.

Inclusion Criteria CLBP:

• Age range 20-70 so that the age distribution is likely to roughly match the healthy normals
• Predominantly axial cLBP meeting the NIH definition of cLBP (at least 3 months on a daily basis) with a MP component (determined by a standardized clinical exam)
• Average pain score of > 3/10, with low back pain being the primary pain site
• cLBP meeting Quebec Task Force Classification System categories I-III (from axial pain only to pain radiating beyond the knee without neurological signs).

Exclusion Criteria CLBP:

• Back surgery within the past year
• Active worker's compensation or litigation claims since these patients are more likely to have exaggerated pain behavior
• New pain treatments within 2 weeks of enrollment
• Any clinically unstable systemic illness that is judged to interfere with the trial
• Non-ambulatory status
• Not able to complete the questionnaires.

The investigators are including healthy normal and cLBP subjects from ages 20 to 70.